Most AI projects stall somewhere between a promising Jupyter notebook and production. We take messy, real-world data from Scottish enterprises, build models around it, and deploy inference pipelines that run 24/7 without babysitting.
Send us your dataset challenge
We build regression and classification models on your historical records. Demand forecasting for retail, churn prediction for subscription platforms, fault detection for manufacturing lines. Each model ships with an API endpoint and a monitoring dashboard that flags drift before accuracy drops.
From quality inspection on food production conveyors to reading handwritten forms in legal archives, our vision pipelines handle annotation, training, and edge deployment. We favour ONNX runtimes so the model runs on a £40 board, not a £4,000 GPU.
Ticket routing, contract clause extraction, sentiment scoring on customer feedback. We fine-tune transformer architectures on domain-specific corpora so the model understands your jargon, not just generic English. Average turnaround from labelled data to staging environment: six weeks.
A model is worthless if the data feeding it arrives late or dirty. We design ingestion, cleaning and feature-store pipelines using Airflow, dbt and BigQuery (or Postgres, depending on your stack). Pipelines include automated tests that catch schema changes before they break inference.
We spend two days inside your existing databases, spreadsheets, or APIs. The output is a short report listing what is usable now, what needs cleaning, and what is missing entirely. No charge for this step if we move forward together.
In three weeks we build a minimal model on a representative sample. You see real metrics: precision, recall, F1. If the numbers do not justify going further, we tell you honestly and you owe nothing beyond the sprint fee.
The winning architecture gets containerised, tested under load, and deployed behind an API gateway. We write integration docs your own developers can follow. Typical timeline here is four to eight weeks depending on data volume and compliance requirements.
Models decay. We set up automated drift detection that compares live predictions against ground truth on a rolling window. When accuracy slips past an agreed threshold, a retraining job fires and a human reviews the candidate model before promotion.
Describe the problem, the data you have, and any deadline. We reply within one working day.
